Simulation-based inference in JAX
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Updated
Sep 24, 2024 - Python
Simulation-based inference in JAX
Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
The official code repo for HyperAgent algorithm published in ICML 2024.
Trabajo de Fin de Grado de Física 2022
A Python package for likelihood-free inference (LFI) methods such as Approximate Bayesian Computation (ABC)
Simulation-based inference using SSNL
A simulation model for the digital reconstruction of 3D root system architectures. Integrated with a simulation-based inference generative deep learning model.
Simple model class and non-vectorized samplers for Approximate Bayesian Statistics (ABC)
This repo contains code that implements vPET-ABC. Currently, we have included Python code attempting GPU acceleration on FDG compartment models.
Application of rejection sampling and markov chain monte carlo (MCMC) algorithms to approximate bayesian computation (ABC). The project includes application of ABC to model the pharmacokinetics of theophylline.
Comparison of summary statistic selection methods with a unifying perspective.
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